Название: Intelligent Algorithms: Theory and Practice
Автор: Han Huang, Zhifeng Hao
Издательство: Elsevier/China Machine Press
Год: 2024
Страниц: 253
Язык: английский
Формат: pdf (true), epub
Размер: 23.3 MB

The Theory and Practice of Intelligent Algorithms discusses the latest achievements of the computation time analysis theory and practical applications of intelligent algorithms. In five chapters, the book covers (1) New methods of intelligent algorithm computation time analysis; (2)Application of intelligent algorithms in computer vision; (3) Application of intelligent algorithms in logistics scheduling; (4) Application of intelligent algorithms in software testing; and (5) Application of intelligent algorithm in multi-objective optimization.

Intelligent algorithms are the algorithmic forms of computational intelligence methods. These algorithms are the common technical methods used by Artificial Intelligence (AI) in intelligent perception, intelligent decision-making, and intelligent planning. At this stage, intelligent algorithms are usually manifested as evolutionary computation, swarm intelligence, Machine Learning, other algorithms, and their combinations. Existing literature on intelligent algorithms focuses on theories, methods, and simulation experiments, leaving practical applications underexplored, especially the application of cutting-edge research in Artificial Intelligence. This book aims to serve as a reference book with practical cases for practitioners of intelligent algorithms based on cutting-edge research results.

In the field of computer vision, the application of Deep Learning methods is more common; however, there are few reports on the application of intelligent optimization algorithms and their combination with Deep Learning methods. The first chapter of this book starts with a basic technical problem of computer vision, that is, image matting, and introduces research cases of intelligent algorithms from mathematical modeling and algorithmic design to engineering applications. Image matting is the key technology and basic method of image processing and video analysis. The breakthrough in accuracy and speed will greatly improve the technical performance of subsequent applications. This book introduces a variety of intelligent algorithms, including fuzzy multiobjective evolution algorithms. It also introduces the application of image matting algorithms in the preprocessing of Deep Learning training data (face detection, face recognition, pedestrian detection, and so on), which can provide implications for technology research and development in computer vision.

Software testing is a necessary but laborious technical activity of software development in the field of software engineering. The technology of software test case generation can save considerable costs of manual operation in program unit testing. However, the test case coding space of actual software programs is often large, resulting in high computational costs of test case generation technology. Based on the idea of heuristic optimization, this book introduces intelligent algorithm strategies such as adaptive evaluation function and association matrix. These strategies realize the optimal allocation of intelligent algorithm computational resources, thus significantly improving the performance of automated software test case generation algorithm. The actual effect of intelligent algorithms has passed the experimental verification of actual software toolkits and public programs such as fog computing, natural language processing (NLP), and blockchain smart contracts, thus providing a reference for the research and development of intelligent optimization software engineering technology.

In addition to a few applications, this book introduces the latest application of multiobjective optimization intelligent algorithms in software configuration, hoping to provide implications for researchers and technicians who use multiobjective optimization algorithms to solve practical complex optimization problems. The last chapter introduces the computation time estimation method that bridges the gap between theoretical research and practical application, offering theoretical support for the application of intelligent algorithms and providing a practical tool for time complexity analysis.

Key Features:
Integrates the theoretical analysis results of intelligent algorithms, which is convenient for the majority of researchers to deeply understand the theoretical analysis results of intelligent algorithms and further supplement and improve the theoretical research of intelligent algorithms
Opens up readers' understanding of the theoretical level of intelligent algorithms and spreads the inherent charm of intelligent algorithms
Integrates the diverse knowledge of society and provides a more comprehensive and scientific knowledge of intelligent algorithm theory